db_index=True

在 Django 模型中，`db_index=True` 是一个用于字段的选项，用于指示数据库在该字段上创建索引。

索引是用于加快数据库查询的一种数据结构。当你在一个字段上设置了 `db_index=True`，Django 会在数据库中为该字段创建一个索引。这样，在执行查询时，数据库可以更快地定位到包含特定值的行，提高查询性能。

举个例子，假设有一个名为 `User` 的模型类，其中有一个 `username` 字段，你希望在该字段上创建索引。可以使用 `db_index=True` 选项来实现：

from django.db import models

class User(models.Model):
    username = models.CharField(max_length=50, db_index=True)
    # 其他字段...

在上述示例中，`username` 字段被标记为 `db_index=True`，这将导致 Django 在数据库中创建一个索引来加速对 `username` 字段的查询操作。

注意：尽管索引可以加快查询速度，但它们也会占用额外的存储空间和对写操作（如插入、更新和删除）的性能有一定的影响。因此，在为字段设置索引时需要权衡考虑。只在需要频繁查询或排序的字段上使用索引，并避免过度索引。

相关问题

class OperationLog(db.Model): """操作日志表""" __bind_key__ = 'db2' __tablename__ = 'operation_log' id = mapped_column(Integer, primary_key=True) userid = mapped_column(String(16)) username = mapped_column(String(32), index=True) endpoint = mapped_column(String(128)) methods = mapped_column(String(16)) full_path = mapped_column(String(256)) c_date = mapped_column(DateTime) @staticmethod def add_operation_log(userid: str, username: str): """新增操作日志""" c_date = datetime.now() operation_log = OperationLog() operation_log.userid = userid operation_log.username = username operation_log.endpoint = request.endpoint operation_log.methods = request.method operation_log.full_path = request.full_path operation_log.c_date = c_date db.session.add(operation_log) db.session.execute( update(User).where(User.userid == userid).values(lastlogin=c_date)) db.session.commit() return True 帮我优化并建议这段代码

以下是对代码的优化和建议：

1. 使用类方法代替静态方法

在这种情况下，我们可以使用类方法代替静态方法。这样，我们可以从类本身传递参数而不是从实例传递参数，并且可以轻松地访问类属性。

2. 使用参数注释

为了更好地理解代码，我们建议使用参数注释，以便将参数名称，类型和说明文档化。

3. 使用类型提示

在函数参数和返回值中使用类型提示可以提高代码的可读性和可维护性，并帮助 IDE 提供更好的代码补全和错误检查。

4. 使用 try-finally 块

使用 try-finally 块可以确保无论是否出现异常，都会关闭数据库会话。

5. 使用 with 语句

使用 with 语句可以确保数据库会话在使用后被关闭，而无需显式调用 commit 或 rollback 方法。

以下是优化后的代码：

class OperationLog(db.Model):
    """操作日志表"""

    __bind_key__ = 'db2'
    __tablename__ = 'operation_log'

    id = mapped_column(Integer, primary_key=True)
    userid = mapped_column(String(16))
    username = mapped_column(String(32), index=True)
    endpoint = mapped_column(String(128))
    methods = mapped_column(String(16))
    full_path = mapped_column(String(256))
    c_date = mapped_column(DateTime)

    @classmethod
    def add_operation_log(cls, userid: str, username: str) -> bool:
        """新增操作日志"""
        c_date = datetime.now()

        operation_log = cls()
        operation_log.userid = userid
        operation_log.username = username
        operation_log.endpoint = request.endpoint
        operation_log.methods = request.method
        operation_log.full_path = request.full_path
        operation_log.c_date = c_date

        with db.session.begin():
            db.session.add(operation_log)
            db.session.execute(
                update(User).where(User.userid == userid).values(lastlogin=c_date))

        return True

LDAM损失函数pytorch代码如下：class LDAMLoss(nn.Module): def init(self, cls_num_list, max_m=0.5, weight=None, s=30): super(LDAMLoss, self).init() m_list = 1.0 / np.sqrt(np.sqrt(cls_num_list)) m_list = m_list * (max_m / np.max(m_list)) m_list = torch.cuda.FloatTensor(m_list) self.m_list = m_list assert s > 0 self.s = s if weight is not None: weight = torch.FloatTensor(weight).cuda() self.weight = weight self.cls_num_list = cls_num_list def forward(self, x, target): index = torch.zeros_like(x, dtype=torch.uint8) index_float = index.type(torch.cuda.FloatTensor) batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(1,0)) # 0,1 batch_m = batch_m.view((16, 1)) # size=(batch_size, 1) (-1,1) x_m = x - batch_m output = torch.where(index, x_m, x) if self.weight is not None: output = output * self.weight[None, :] target = torch.flatten(target) # 将 target 转换成 1D Tensor logit = output * self.s return F.cross_entropy(logit, target, weight=self.weight) 模型部分参数如下：# 设置全局参数 model_lr = 1e-5 BATCH_SIZE = 16 EPOCHS = 50 DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') use_amp = True use_dp = True classes = 7 resume = None CLIP_GRAD = 5.0 Best_ACC = 0 #记录最高得分 use_ema=True model_ema_decay=0.9998 start_epoch=1 seed=1 seed_everything(seed) # 数据增强 mixup mixup_fn = Mixup( mixup_alpha=0.8, cutmix_alpha=1.0, cutmix_minmax=None, prob=0.1, switch_prob=0.5, mode='batch', label_smoothing=0.1, num_classes=classes) # 读取数据集 dataset_train = datasets.ImageFolder('/home/adminis/hpy/ConvNextV2_Demo/RAF-DB/RAF/train', transform=transform) dataset_test = datasets.ImageFolder("/home/adminis/hpy/ConvNextV2_Demo/RAF-DB/RAF/valid", transform=transform_test)# 导入数据 train_loader = torch.utils.data.DataLoader(dataset_train, batch_size=BATCH_SIZE, shuffle=True,drop_last=True) test_loader = torch.utils.data.DataLoader(dataset_test, batch_size=BATCH_SIZE, shuffle=False) 帮我用pytorch实现模型在模型训练中使用LDAM损失函数

好的，以下是使用LDAM损失函数训练模型的Pytorch代码：

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from torchvision import datasets, transforms
from ldam_loss import LDAMLoss

# 定义模型
class MyModel(nn.Module):
    def __init__(self, num_classes):
        super(MyModel, self).__init__()
        self.num_classes = num_classes
        self.features = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
        )
        self.classifier = nn.Sequential(
            nn.Linear(128 * 4 * 4, 256),
            nn.ReLU(inplace=True),
            nn.Linear(256, num_classes),
        )

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return x

# 设置超参数
model_lr = 1e-4
BATCH_SIZE = 16
EPOCHS = 50
DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
use_amp = True
use_dp = True
classes = 7
resume = None
CLIP_GRAD = 5.0
Best_ACC = 0
use_ema = True
model_ema_decay = 0.9998
start_epoch = 1
seed = 1

# 设置随机种子
def seed_everything(seed):
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    np.random.seed(seed)
seed_everything(seed)

# 定义数据增强
transform = transforms.Compose([
    transforms.Resize(224),
    transforms.RandomHorizontalFlip(),
    transforms.RandomRotation(10),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225])
])
transform_test = transforms.Compose([
    transforms.Resize(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225])
])

# 定义数据集
dataset_train = datasets.ImageFolder('/home/adminis/hpy/ConvNextV2_Demo/RAF-DB/RAF/train', transform=transform)
dataset_test = datasets.ImageFolder("/home/adminis/hpy/ConvNextV2_Demo/RAF-DB/RAF/valid", transform=transform_test)

# 定义数据加载器
train_loader = torch.utils.data.DataLoader(dataset_train, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)
test_loader = torch.utils.data.DataLoader(dataset_test, batch_size=BATCH_SIZE, shuffle=False)

# 定义模型及优化器
model = MyModel(num_classes=classes).to(DEVICE)
optimizer = torch.optim.Adam(model.parameters(), lr=model_lr)

# 使用LDAM损失函数
cls_num_list = [dataset_train.targets.count(i) for i in range(classes)]
criterion = LDAMLoss(cls_num_list=cls_num_list, max_m=0.5, weight=None, s=30)

# 训练模型
for epoch in range(start_epoch, EPOCHS+1):
    model.train()
    for i, (data, target) in enumerate(train_loader):
        data, target = data.to(DEVICE), target.to(DEVICE)
        mixup_data, mixup_target = mixup_fn(data, target)  # 数据增强
        optimizer.zero_grad()
        output = model(mixup_data)
        loss = criterion(output, mixup_target)
        if use_dp:
            loss.backward()
            torch.nn.utils.clip_grad_norm_(model.parameters(), CLIP_GRAD)
        else:
            with amp.scale_loss(loss, optimizer) as scaled_loss:
                scaled_loss.backward()
                torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), CLIP_GRAD)
        optimizer.step()
    if use_ema:
        ema_model = ModelEMA(model, decay=model_ema_decay)
        ema_model.update(model)
    else:
        ema_model = None
    test_acc = test(model, test_loader, DEVICE)
    if test_acc > Best_ACC:
        Best_ACC = test_acc
        save_checkpoint({
            'epoch': epoch,
            'state_dict': model.state_dict(),
            'optimizer': optimizer.state_dict(),
            'Best_ACC': Best_ACC,
        }, is_best=True)